Minimally Invasive Left Ventricular Assist Device Implantation: A Systematic Review of Current Evidence on Clinical Outcomes and Surgical Approaches
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Minimally invasive cardiac surgical (MICS) approaches to the implantation of left ventricular assist devices (LVADs) have gained increasing interest as alternatives to full median sternotomy (FS), particularly in patients with prior cardiac surgeries or elevated surgical risk. However, evidence regarding their safety, feasibility, and clinical outcomes remains fragmented. This systematic review aimed to evaluate the effectiveness and safety of minimally invasive techniques for LVAD implantation in comparison to standard sternotomy, with a focus on mortality, perioperative complications, intensive care unit (ICU) stay, and infection rates. METHODS: A comprehensive literature search was conducted in PubMed, Web of Science, Science Direct, Cochrane Library, and Google Scholar up to 1 January 2025. Studies were included if they reported on adult patients undergoing LVAD implantation via minimally invasive thoracotomy or sternotomy-sparing approaches, with or without comparator groups. Data were extracted and synthesized qualitatively; the Newcastle-Ottawa Scale (NOS) was applied to assess the methodological quality of the included cohort and retrospective comparative studies. RESULTS: A total of 12 studies involving 1448 patients were included (584 received MICS and 862 received FS). MICS techniques have demonstrated comparable short and mid-term survival outcomes, with trends toward reduced ICU stay, fewer reoperations for bleeding, and lower incidence of driveline infections. Some studies reported longer operative and cardiopulmonary bypass times in the MICS group. Among high-risk cohorts, such as patients with prior sternotomies or significant comorbidities, MICS was associated with lower morbidity and acceptable safety profiles. However, heterogeneity in patient selection, surgical protocols, and outcome definitions limited quantitative synthesis. CONCLUSIONS: Minimally invasive LVAD implantation is a viable alternative to conventional sternotomy in selected patient populations. While current data suggest favorable perioperative outcomes and equivalent survival, high-quality prospective studies are needed to confirm long-term benefits and to guide patient selection. MICS approaches should be considered within multidisciplinary teams experienced in advanced heart failure surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".